OpenRaiser/Intern-Atlas huggingface dataset:
Performance Evaluation of User Scheduling for Full-Duplex Small Cells in Ultra-Dense Networks
Venue: arXiv cs.IT 2016
Year: 2016

Full-duplex (FD) communication is an emerging technology that can potentially double the throughput of cellular networks. Preliminary studies in single-cell or small FD network deployments have revealed promising rate gains using self-interference cancellation (SIC) techniques and receive processing. Nevertheless, the system-level performance gains of FD small cells in ultra-dense networks (UDNs) have not been fully investigated yet. In this paper, we evaluate the performance of resource allocation in ultra-dense FD small-cell networks using spatial stochastic models for the network layout and 3GPP channel models. More specifically, we consider various UDN scenarios and assess the performance of different low-complexity user-scheduling schemes and power allocation between uplink and downlink. We also provide useful insights into the effect of the SIC capability on the network throughput.
Notes on Lynch-Morawska Systems
Venue: arXiv cs.LO 2016
Year: 2016

In this paper we investigate convergent term rewriting systems that conform to the criteria set out by Christopher Lynch and Barbara Morawska in their seminal paper "Basic Syntactic Mutation." The equational unification problem modulo such a rewrite system is solvable in polynomial-time. In this paper, we derive properties of such a system which we call an $LM$-system. We show, in particular, that the rewrite rules in an $LM$-system have no left- or right-overlaps. We also show that despite the restricted nature of an $LM$-system, there are important undecidable problems, such as the deduction problem in cryptographic protocol analysis (also called the the cap problem) that remain undecidable for $LM$-systems.
Location-Based Beamforming and Physical Layer Security in Rician Wiretap Channels
Venue: arXiv cs.IT 2016
Year: 2016

We propose a new location-based beamforming (LBB) scheme for wiretap channels, where a multi-antenna source communicates with a single-antenna legitimate receiver in the presence of a multi-antenna eavesdropper. We assume that all channels are in a Rician fading environment, the channel state information from the legitimate receiver is perfectly known at the source, and that the only information on the eavesdropper available at the source is her location. We first describe how the optimal beamforming vector that minimizes the secrecy outage probability of the system is obtained, illustrating its dependence on the eavesdropper's location. We then derive an easy-to-compute expression for the secrecy outage probability when our proposed LBB scheme is adopted. We also consider the positive impact a friendly jammer can have on our beamforming solution, showing how the path to optimality remains the same. Finally, we investigate the impact of location uncertainty on the secrecy outage probability, showing how our solution can still allow for secrecy even when the source only has a noisy estimate of the eavesdropper's location. Our work demonstrates how a multi-antenna array, operating in the most general channel conditions and most likely system set-up, can be configured rapidly in the field so as to deliver an optimal physical layer security solution.
The THUMOS Challenge on Action Recognition for Videos "in the Wild"
Venue: arXiv cs.CV 2016
Year: 2016

Automatically recognizing and localizing wide ranges of human actions has crucial importance for video understanding. Towards this goal, the THUMOS challenge was introduced in 2013 to serve as a benchmark for action recognition. Until then, video action recognition, including THUMOS challenge, had focused primarily on the classification of pre-segmented (i.e., trimmed) videos, which is an artificial task. In THUMOS 2014, we elevated action recognition to a more practical level by introducing temporally untrimmed videos. These also include `background videos' which share similar scenes and backgrounds as action videos, but are devoid of the specific actions. The three editions of the challenge organized in 2013--2015 have made THUMOS a common benchmark for action classification and detection and the annual challenge is widely attended by teams from around the world. In this paper we describe the THUMOS benchmark in detail and give an overview of data collection and annotation procedures. We present the evaluation protocols used to quantify results in the two THUMOS tasks of action classification and temporal detection. We also present results of submissions to the THUMOS 2015 challenge and review the participating approaches. Additionally, we include a comprehensive empirical study evaluating the differences in action recognition between trimmed and untrimmed videos, and how well methods trained on trimmed videos generalize to untrimmed videos. We conclude by proposing several directions and improvements for future THUMOS challenges.
The Dynamics of Initiative in Communication Networks
Venue: arXiv cs.SI 2016
Year: 2016

Human social interaction is often intermittent. Two acquainted persons can have extended periods without social interaction punctuated by periods of repeated interaction. In this case, the repeated interaction can be characterized by a seed initiative by either of the persons and a number of follow-up interactions. The tendency to initiate social interaction plays an important role in the formation of social networks and is in general not symmetric between persons. In this paper, we study the dynamics of initiative by analysing and modeling a detailed call and text message network sampled from a group of 700 individuals. We show that in an average relationship between two individuals, one part is almost twice as likely to initiate communication compared to the other part. The asymmetry has social consequences and ultimately might lead to the discontinuation of a relationship. We explain the observed asymmetry by a positive feedback mechanism where individuals already taking initiative are more likely to take initiative in the future. In general, people with many initiatives receive attention from a broader spectrum of friends than people with few initiatives. Lastly, we compare the likelihood of taking initiative with the basic personality traits of the five factor model.
Asymptotic and Finite Frame Length Analysis of Frame Asynchronous Coded Slotted ALOHA
Venue: arXiv cs.IT 2016
Year: 2016

We consider a frame-asynchronous coded slotted ALOHA (FA-CSA) system where users become active according to a Poisson random process. In contrast to standard frame-synchronous CSA (FS-CSA), users transmit a first replica of their message in the slot following their activation and other replicas uniformly at random in a number of subsequent slots. We derive the (approximate) density evolution that characterizes the asymptotic performance of FA-CSA when the frame length goes to infinity. We show that, if users can monitor the system before they start transmitting, a boundary-effect similar to that of spatially-coupled codes occurs, which greatly improves the decoding threshold as compared to FS-CSA. We also derive analytical approximations of the error floor (EF) in the finite frame length regime. We show that FA-CSA yields in general lower EF, better performance in the waterfall region, and lower average delay, as compared to FS-CSA.
Analysis of the Entropy-guided Switching Trimmed Mean Deviation-based Anisotropic Diffusion filter
Venue: arXiv cs.CV 2016
Year: 2016

This report describes the experimental analysis of a proposed switching filter-anisotropic diffusion hybrid for the filtering of the fixed value (salt and pepper) impulse noise (FVIN). The filter works well at both low and high noise densities though it was specifically designed for high noise density levels. The filter combines the switching mechanism of decision-based filters and the partial differential equation-based formulation to yield a powerful system capable of recovering the image signals at very high noise levels. Experimental results indicate that the filter surpasses other filters, especially at very high noise levels. Additionally, its adaptive nature ensures that the performance is guided by the metrics obtained from the noisy input image. The filter algorithm is of both global and local nature, where the former is chosen to reduce computation time and complexity, while the latter is used for best results.
Weighted domination number of cactus graphs
Venue: arXiv cs.DS 2016
Year: 2016

In the paper, we write a linear algorithm for calculating the weighted domination number of a vertex-weighted cactus. The algorithm is based on the well known depth first search (DFS) structure. Our algorithm needs less than $12n+5b$ additions and $9n+2b$ $\min$-operations where $n$ is the number of vertices and $b$ is the number of blocks in the cactus.
Wi-Fi Scaling and Performance in Dense Frequency Planned Networks
Venue: arXiv cs.NI 2016
Year: 2016

The capacity scaling of a Wi-Fi network with fixed transmit power is investigated. The 802.11 MAC appears to impose a hard limit on the area capacity by enforcing single transmissions in overlapped cell coverage areas. However due to the difficulty reading the packet header when transmissions either overlap or are faded, stations in different cells may lose synchronization with each other. A large frequency planned network with full propagation impairments of fast and slow fading is simulated in NS-3 with an upgraded receiver model supporting packet capture. Downlink Wi-Fi performance is compared to an equivalent LTE network on the same cell centers. Results show that Wi-Fi downlink throughput grows with densification limited by decreasing SINR and partially operational virtual carrier sense. LTE area capacity grows faster as it is only constrained by SINR.
Online Action Detection
Venue: arXiv cs.CV 2016
Year: 2016

In online action detection, the goal is to detect the start of an action in a video stream as soon as it happens. For instance, if a child is chasing a ball, an autonomous car should recognize what is going on and respond immediately. This is a very challenging problem for four reasons. First, only partial actions are observed. Second, there is a large variability in negative data. Third, the start of the action is unknown, so it is unclear over what time window the information should be integrated. Finally, in real world data, large within-class variability exists. This problem has been addressed before, but only to some extent. Our contributions to online action detection are threefold. First, we introduce a realistic dataset composed of 27 episodes from 6 popular TV series. The dataset spans over 16 hours of footage annotated with 30 action classes, totaling 6,231 action instances. Second, we analyze and compare various baseline methods, showing this is a challenging problem for which none of the methods provides a good solution. Third, we analyze the change in performance when there is a variation in viewpoint, occlusion, truncation, etc. We introduce an evaluation protocol for fair comparison. The dataset, the baselines and the models will all be made publicly available to encourage (much needed) further research on online action detection on realistic data.
The Mean Partition Theorem of Consensus Clustering
Venue: arXiv cs.LG 2016
Year: 2016

To devise efficient solutions for approximating a mean partition in consensus clustering, Dimitriadou et al. [3] presented a necessary condition of optimality for a consensus function based on least square distances. We show that their result is pivotal for deriving interesting properties of consensus clustering beyond optimization. For this, we present the necessary condition of optimality in a slightly stronger form in terms of the Mean Partition Theorem and extend it to the Expected Partition Theorem. To underpin its versatility, we show three examples that apply the Mean Partition Theorem: (i) equivalence of the mean partition and optimal multiple alignment, (ii) construction of profiles and motifs, and (iii) relationship between consensus clustering and cluster stability.
Derivative-Based Diagnosis of Regular Expression Ambiguity
Venue: arXiv cs.FL 2016
Year: 2016

Regular expressions are often ambiguous. We present a novel method based on Brzozowski's derivatives to aid the user in diagnosing ambiguous regular expressions. We introduce a derivative-based finite state transducer to generate parse trees and minimal counter-examples. The transducer can be easily customized to either follow the POSIX or Greedy disambiguation policy and based on a finite set of examples it is possible to examine if there are any differences among POSIX and Greedy.
An Optimized Combination of a Large Grid Connected PV System along with Battery Cells and a Diesel Generator
Venue: arXiv cs.SY 2016
Year: 2016

Environmental, economical and technical benefits of photovoltaic (PV) systems make them to be used in many countries. The main characteristic of PV systems is the fluctuations of their output power. Hence, high penetration of PV systems into electric network could be detrimental to overall system performance. Furthermore, the fluctuations in the output power of PV systems make it difficult to predict their output, and to consider them in generation planning of the units. The main objective of this paper is to propose a hybrid method which can be used to control and reduce the power fluctuations generated from large grid- connected PV systems. The proposed method focuses on using a suitable storage battery along with curtailment of the generated power by operating the PV system below the maximum power point (MPP) and deployment of a diesel generator. These methods are analyzed to investigate the impacts of implementing them on the economical benefits that the PV system owner could gain. To maximize the revenues, an optimization problem is solved.
Differential Reprogramming Based on Constructive Interference for Wireless Sensor Network
Venue: arXiv cs.NI 2016
Year: 2016

To improve the performance of reprogramming in wireless sensor network, we present a novel reprogramming structure and constructive interference-based dissemination protocol (CIDP) to transmit the patch through out the network fast and reliability. CIDP disseminates the patch, which is divided into several packets, to the network exploiting constructive interference. We evaluate our implementation of CIDP using simulation under different number of nodes. Our results show that CIDP disseminates the patch less than 4 milliseconds. In general, the probability of a node receives the complete patch as high as 99.99%.
Utilitarian Distributed Constraint Optimization Problems
Venue: arXiv cs.AI 2016
Year: 2016

Privacy has been a major motivation for distributed problem optimization. However, even though several methods have been proposed to evaluate it, none of them is widely used. The Distributed Constraint Optimization Problem (DCOP) is a fundamental model used to approach various families of distributed problems. As privacy loss does not occur when a solution is accepted, but when it is proposed, privacy requirements cannot be interpreted as a criteria of the objective function of the DCOP. Here we approach the problem by letting both the optimized costs found in DCOPs and the privacy requirements guide the agents' exploration of the search space. We introduce Utilitarian Distributed Constraint Optimization Problem (UDCOP) where the costs and the privacy requirements are used as parameters to a heuristic modifying the search process. Common stochastic algorithms for decentralized constraint optimization problems are evaluated here according to how well they preserve privacy. Further, we propose some extensions where these solvers modify their search process to take into account their privacy requirements, succeeding in significantly reducing their privacy loss without significant degradation of the solution quality.
Validation of computer simulations of the HyQ robot
Venue: arXiv cs.RO 2016
Year: 2016

This short technical report illustrates the results of a test procedure we performed to validate the computer simulation of the HyQ robot.
How to Diagonalize a MIMO Channel with Arbitrary Transmit Covariance?
Venue: arXiv cs.IT 2016
Year: 2016

Multiple-input multiple-output (MIMO) or multi-antenna communication is a key technique to achieve high spectral efficiency in wireless systems. For the point-to-point MIMO channel, it is a well-known result that the channel singular value decomposition (SVD) based linear precoding and decoding achieves the channel capacity, which also diagonalizes the MIMO channel into parallel single-input single-output (SISO) sub-channels for independent encoding and decoding. However, in multi-user MIMO systems, the optimal transmit covariance of each MIMO link is generally not its channel SVD based as a result of the control and balance of the co-channel interference among users. Thus, it remains unknown whether the linear precoding/decoding strategy is still able to achieve the capacity of each MIMO link and yet diagonalize its MIMO channel, with a given set of optimal transmit covariance of all users. This letter solves this open problem by providing a closed-form capacity-achieving linear precoder/decoder design that diagonalizes a MIMO channel with arbitrary transmit covariance. Numerical examples are also provided to validate the proposed solution in various multi-user MIMO systems.
Memory and Information Processing in Recurrent Neural Networks
Venue: arXiv cs.NE 2016
Year: 2016

Recurrent neural networks (RNN) are simple dynamical systems whose computational power has been attributed to their short-term memory. Short-term memory of RNNs has been previously studied analytically only for the case of orthogonal networks, and only under annealed approximation, and uncorrelated input. Here for the first time, we present an exact solution to the memory capacity and the task-solving performance as a function of the structure of a given network instance, enabling direct determination of the function--structure relation in RNNs. We calculate the memory capacity for arbitrary networks with exponentially correlated input and further related it to the performance of the system on signal processing tasks in a supervised learning setup. We compute the expected error and the worst-case error bound as a function of the spectra of the network and the correlation structure of its inputs and outputs. Our results give an explanation for learning and generalization of task solving using short-term memory, which is crucial for building alternative computer architectures using physical phenomena based on the short-term memory principle.
Limits to Verification and Validation of Agentic Behavior
Venue: arXiv cs.AI 2016
Year: 2016

Verification and validation of agentic behavior have been suggested as important research priorities in efforts to reduce risks associated with the creation of general artificial intelligence (Russell et al 2015). In this paper we question the appropriateness of using language of certainty with respect to efforts to manage that risk. We begin by establishing a very general formalism to characterize agentic behavior and to describe standards of acceptable behavior. We show that determination of whether an agent meets any particular standard is not computable. We discuss the extent of the burden associated with verification by manual proof and by automated behavioral governance. We show that to ensure decidability of the behavioral standard itself, one must further limit the capabilities of the agent. We then demonstrate that if our concerns relate to outcomes in the physical world, attempts at validation are futile. Finally, we show that layered architectures aimed at making these challenges tractable mistakenly equate intentions with actions or outcomes, thereby failing to provide any guarantees. We conclude with a discussion of why language of certainty should be eradicated from the conversation about the safety of general artificial intelligence.
Sparse Representations of Clifford and Tensor algebras in Maxima
Venue: arXiv cs.SC 2016
Year: 2016

Clifford algebras have broad applications in science and engineering. The use of Clifford algebras can be further promoted in these fields by availability of computational tools that automate tedious routine calculations. We offer an extensive demonstration of the applications of Clifford algebras in electromagnetism using the geometric algebra G3 = Cl(3,0) as a computational model in the Maxima computer algebra system. We compare the geometric algebra-based approach with conventional symbolic tensor calculations supported by Maxima, based on the itensor package. The Clifford algebra functionality of Maxima is distributed as two new packages called clifford - for basic simplification of Clifford products, outer products, scalar products and inverses; and cliffordan - for applications of geometric calculus.
On principles of large deviation and selected data compression
Venue: arXiv cs.IT 2016
Year: 2016

The Shannon Noiseless coding theorem (the data-compression principle) asserts that for an information source with an alphabet $\mathcal X=\{0,\ldots ,\ell -1\}$ and an asymptotic equipartition property, one can reduce the number of stored strings $(x_0,\ldots ,x_{n-1})\in {\mathcal X}^n$ to $\ell^{nh}$ with an arbitrary small error-probability. Here $h$ is the entropy rate of the source (calculated to the base $\ell$). We consider further reduction based on the concept of utility of a string measured in terms of a rate of a weight function. The novelty of the work is that the distribution of memory is analyzed from a probabilistic point of view. A convenient tool for assessing the degree of reduction is a probabilistic large deviation principle. Assuming a Markov-type setting, we discuss some relevant formulas, including the case of a general alphabet.
PFO: A Parallel Friendly High Performance System for Online Query and Update of Nearest Neighbors
Venue: arXiv cs.DC 2016
Year: 2016

Nearest Neighbor(s) search is the fundamental computational primitive to tackle massive dataset. Locality Sensitive Hashing (LSH) has been a bracing tool for Nearest Neighbor(s) search in high dimensional spaces. However, traditional LSH systems cannot be applied in online big data systems to handle a large volume of query/update requests, because most of the systems optimize the query efficiency with the assumption of infrequent updates and missing the parallel-friendly design. As a result, the state-of-the-art LSH systems cannot adapt the system response to the user behavior interactively. In this paper, we propose a new LSH system called PFO. It handles query/update requests in RAM and scales the system capacity by using flash memory. To achieve high streaming data throughput, PFO adopts a parallel-friendly indexing structure while preserving the distance between data points. Further, it accommodates inbound data in real-time and dispatches update requests intelligently to eliminate the cross-threads synchronization. We carried out extensive evaluations with large synthetic and standard benchmark datasets. Results demonstrate that PFO delivers shorter latency and offers scalable capacity compared with the existing LSH systems. PFO serves with higher throughput than the state-of-the-art LSH indexing structure when dealing with online query/update requests to nearest neighbors. Meanwhile, PFO returns neighbors with much better quality, thus being efficient to handle online big data applications, e.g. streaming recommendation system, interactive machine learning systems.
Unsupervised Representation Learning of Structured Radio Communication Signals
Venue: arXiv cs.LG 2016
Year: 2016

We explore unsupervised representation learning of radio communication signals in raw sampled time series representation. We demonstrate that we can learn modulation basis functions using convolutional autoencoders and visually recognize their relationship to the analytic bases used in digital communications. We also propose and evaluate quantitative met- rics for quality of encoding using domain relevant performance metrics.